Results 21 to 30 of about 152,362 (267)
SIMLR: Machine Learning inside the SIR Model for COVID-19 Forecasting
Forecasting, 2022 Accurate forecasts of the number of newly infected people during an epidemic are critical for making effective timely decisions. This paper addresses this challenge using the SIMLR model, which incorporates machine learning (ML) into the epidemiological ...
Roberto Vega +2 more
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Probabilistic knowledge-based characterization of conceptual geological models
Applied Computing and Geosciences, 2021 The construction of conceptual geological models is an essential task in petroleum exploration, especially during the early stages of investment, when evidence about the subsurface is limited.
Júlio Hoffimann +11 more
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Generalized Permutohedra from Probabilistic Graphical Models [PDF]
, 2017 A graphical model encodes conditional independence relations via the Markov properties. For an undirected graph these conditional independence relations can be represented by a simple polytope known as the graph associahedron, which can be constructed as
Caroline Uhler +13 more
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Deep Probabilistic Graphical Modeling
CoRR, 2020 Probabilistic graphical modeling (PGM) provides a framework for formulating an interpretable generative process of data and expressing uncertainty about unknowns, but it lacks flexibility. Deep learning (DL) is an alternative framework for learning from data that has achieved great empirical success in recent years.
openaire +3 more sources
From Probabilistic Graphical Models to Generalized Tensor Networks for Supervised Learning
IEEE Access, 2020 Tensor networks have found a wide use in a variety of applications in physics and computer science, recently leading to both theoretical insights as well as practical algorithms in machine learning.
Ivan Glasser +2 more
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Marginal and simultaneous predictive classification using stratified graphical models [PDF]
, 2014 An inductive probabilistic classification rule must generally obey the principles of Bayesian predictive inference, such that all observed and unobserved stochastic quantities are jointly modeled and the parameter uncertainty is fully acknowledged ...
Corander, Jukka +3 more
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On a Class of Tensor Markov Fields
Entropy, 2020 Here, we introduce a class of Tensor Markov Fields intended as probabilistic graphical models from random variables spanned over multiplexed contexts. These fields are an extension of Markov Random Fields for tensor-valued random variables.
Enrique Hernández-Lemus
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An Order-Independent Algorithm for Learning Chain Graphs
Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2021 LWF chain graphs combine directed acyclic graphs and undirected graphs. We propose a PC-like algorithm, called PC4LWF, that finds the structure of chain graphs under the faithfulness assumption to resolve the problem of scalability of the proposed ...
Mohammad Ali Javidian +2 more
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Probabilistic inference in general graphical models through sampling in stochastic networks of spiking neurons. [PDF]
PLoS Computational Biology, 2011 An important open problem of computational neuroscience is the generic organization of computations in networks of neurons in the brain. We show here through rigorous theoretical analysis that inherent stochastic features of spiking neurons, in ...
Dejan Pecevski +2 more
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Bayesian graphical models for computational network biology
BMC Bioinformatics, 2018 Background Computational network biology is an emerging interdisciplinary research area. Among many other network approaches, probabilistic graphical models provide a comprehensive probabilistic characterization of interaction patterns between molecules ...
Yang Ni, Peter Müller, Lin Wei, Yuan Ji
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